Advertisement

Risk Scoring Models for Trade Credit in Small and Medium Enterprises

  • Manuel TerradezEmail author
  • Renatas Kizys
  • Angel A. Juan
  • Ana M. Debon
  • Bartosz Sawik
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 136)

Abstract

Trade credit refers to providing goods and services on a deferred payment basis. Commercial credit management is a matter of great importance for most small and medium enterprises (SMEs), since it represents a significant portion of their assets. Commercial lending involves assuming some credit risk due to exposure to default. Thus, the management of trade credit and payment delays is strongly related to the liquidation and bankruptcy of enterprises. In this paper we study the relationship between trade credit management and the level of risk in SMEs. Despite its relevance for most SMEs, this problem has not been sufficiently analyzed in the existing literature. After a brief review of existing literature, we use a large database of enterprises to analyze data and propose a multivariate decision-tree model which aims at explaining the level of risk as a function of several variables, both of financial and non-financial nature. Decision trees replace the equation in parametric regression models with a set of rules. This feature is an important aid for the decision process of risk experts, as it allows them to reduce time and then the economic cost of their decisions.

Keywords

Trade credit Scoring models Small and medium enterprises Multivariate regression Decision trees 

Notes

Acknowledgments

This work has been partially supported by the NCN grant (6459/B/T02/2011/40) and AGH grant (11.11.200.274).

References

  1. 1.
    Altman, E.I.: Financial ratios, discriminant analysis and prediction of corporate bank ruptcy. J. Financ. 23, 589–609 (1968)CrossRefGoogle Scholar
  2. 2.
    Altman, E.I., Sabato, G.: Modeling credit risk for SMEs: evidence from the US market. ABACUS 43, 332–357 (2007)CrossRefGoogle Scholar
  3. 3.
    Altman, E.I., Sabato, G., Wilson, N.: The value of non-financial information in SME risk management. J. Credit Risk 6, 1–33 (2010)Google Scholar
  4. 4.
    Aziz, A., Emanuel, D.C., Lawson, G.H.: Bankruptcy prediction: an investigation of cash flow based models. J. Manag. Stud. 25, 419–437 (1988)CrossRefGoogle Scholar
  5. 5.
    Becchetti, L., Sierra, J.: Bankruptcy risk and productive efficiency in manufacturing firms. J. Bank. Financ. 27, 2099–2120 (2002)CrossRefGoogle Scholar
  6. 6.
    Berry, M.J.A., Linoff, G.: Data Mining Techniques. Wiley, New York (1997)Google Scholar
  7. 7.
    Boissay, F., Gropp, R.: Payment defaults and interfirm liquidity provision. Rev. Financ. 1–42, (2013), doi: 10.1093/rof/rfs045
  8. 8.
    Cheng, N., Pike, R.: The trade credit decision: evidence of UK firms. Manag. Decis. Econ. 24, 419–438 (2003)CrossRefGoogle Scholar
  9. 9.
    Correa, A., Acosta, M., Gonzalez, A.L.: La insolvencia empresarial: un anlisis emprico para la pyme. Revista de Contabilidad 6, 47–79 (2003)Google Scholar
  10. 10.
    Cunat, V.: Trade credit: suppliers as debt collectors and insurance providers. Rev. Financ. Stud. 20, 491–527 (2007)CrossRefGoogle Scholar
  11. 11.
    Deakin, E.B.: A discriminant analysis of predictors of business failure. J. Account. Res. 10, 167–179 (1972)CrossRefGoogle Scholar
  12. 12.
    Fantazzini, D., Figini, S.: Random survival forest models for SME credit risk measurement. Methodol. Comput. Appl. Probab. 11, 29–45 (2009)CrossRefzbMATHMathSciNetGoogle Scholar
  13. 13.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recognit. Lett. 27, 861–874 (2006)CrossRefGoogle Scholar
  14. 14.
    Grunert, J., Norden, L., Weber, M.: The role of non-financial factors in internal credit ratings. J. Bank. Financ. 29, 509–531 (2004)CrossRefGoogle Scholar
  15. 15.
    Hastie, T, Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, Berlin (2001)Google Scholar
  16. 16.
    Hernandez, J., Ramirez, M.J., Ferri, C.: Introducción a la minería de datos. Pearson Prentice Hall (2004)Google Scholar
  17. 17.
    Lizarraga, F.: Modelos de prevision del fracaso empresarial: funciona entre nuestras empresas el modelo de Altman de 1968? Revista de Contabilidad 1, 137–164 (1998)Google Scholar
  18. 18.
    Metz, C.E., Kronman, H.B.: Statistical significance tests for binormal ROC curves. J. Math. Psychol. 22, 218–243 (1980)CrossRefzbMATHGoogle Scholar
  19. 19.
    Micha, B.: Analysis of business failures in France. J. Bank. Financ. 8, 281–291 (1984)CrossRefGoogle Scholar
  20. 20.
    Ohlson, J.: Financial ratios and the probabilistic prediction of bankruptcy. J. Account. Res. 18, 109–131 (1980)CrossRefGoogle Scholar
  21. 21.
    Poutziouris, P., Michaelas, N., Soufani, K.: Financial management of Trade Credits in SMEs. Working paper. Concordia University. http://www.efmaefm.org/efma2005/papers/241-soufani_paper.pdf
  22. 22.
    Pozuelo, J., Labatut, G., Veres, E.: Análisis descriptivo de los procesos de fracaso empresarial en microempresas mediante técnicas multivariantes. Revista Europea de Direccin y Economa de la Empresa, 19, 47–66 (2010)Google Scholar
  23. 23.
    Rockafellar, R.T., Uryasev, S.: Optimization of conditional value-at-risk. J. Risk 2(3), 21–41 (2000)Google Scholar
  24. 24.
    Rockafellar, R.T., Uryasev, S.: Conditional value-at-risk for general loss distributions. J. Bank. Financ. 26, 1443–1471 (2002)CrossRefGoogle Scholar
  25. 25.
    Sarykalin S., Serraino G., Uryasev S.: Value-at-risk vs. conditional value-at-risk in risk management and optimization. In: Chen, Z-L., Raghavan, S., Gray, P., (Eds.) Tutorials in Operations Research, INFORMS Annual Meeting, Washington DC, USA, October 12–15 (2008)Google Scholar
  26. 26.
    Sawik, B.: Downside risk approach for multi-objective portfolio optimization. In: Klatte, D., Lthi, H.-J., Schmedders, K. (eds.) Operations Research Proceedings 2011, Operations Research Proceedings, pp. 191–196. Springer, Heidelberg (2012)Google Scholar
  27. 27.
    Sobehart, J.R., Keenan, S.C.: A practical review and test of default prediction models. RMA J. 84, 54–59 (2001)Google Scholar
  28. 28.
    Swets, J.A.: Signal Detection Theory and ROC Analysis in Psychology and Diagnostics. Collected Papers Lawrence Erlbaum Associates (1996)Google Scholar
  29. 29.
    Wilner, B.: The exploitation of relationships in financial distress: the case of trade credit. J. Financ. 55, 153–178 (2000)CrossRefGoogle Scholar
  30. 30.
    Zweig, M.H., Campbell, G.: Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin. Chem. 39, 561–577 (1993)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Manuel Terradez
    • 1
    Email author
  • Renatas Kizys
    • 2
  • Angel A. Juan
    • 3
  • Ana M. Debon
    • 1
  • Bartosz Sawik
    • 4
  1. 1.Universitat Politecnica de ValenciaValenciaSpain
  2. 2.Portsmouth UniversityPortsmouthUK
  3. 3.IN3 - Open University of CataloniaBarcelonaSpain
  4. 4.AGH University of Science and TechnologyKrakowPoland

Personalised recommendations